The present invention relates to an information processing system using a neural network learning function. A specific example of such an information processing system includes a pattern recognition system using a neural network learning function.
A neural network simulating the neural network in the brain has such a capability that simply by teaching the network an input data and an expected output value (hereinafter referred to as "the teacher's data") of the network against the particular input data, a recognition algorithm for discriminating and classifying the data into a predetermined category is constructed. This capability has actively promoted an application to the fields of pattern recognition such as image and voice recognitions. Especially in image recognition, however, a great problem has been posed by the requirement for an image-processing expert to develop a recognition algorithm by a heuristic technique each time an object to be recognized changes. If a neural network is used, all that is necessary is to teach the neural network an object of application, and therefore it is possible to simplify and shorten the time for the development of a recognition algorithm.
The conventional neural network system, in a personal computer or a work station, comprises a neural network definition section for defining the construction (number of layers, number of neurons of each layer, etc.) of the neural network and an operating section for producing an associative output having a learning or learned neural network using a learning data (a combination of an input data and a teacher's data) read from a keyboard or a file. Systems of this type are described, for example, in Technical Report PRU 88-58, Sept. 16, 1988, pp. 79-86, The Institute of Electronics, Information and Communication Engineers of Japan and in Nikkei Computer, Mar. 14, 1988, p. 14.
The conventional neural network system, configured of a section for defining the structure of a neural network and an operating section for learning and producing an associative output, lacks full consideration of the preparation of the learning data and application of the learning data to the neural network (for example, it is required that an operating section for learning and producing an associative output be provided for each of the applications such as image and voice recognition, a learning data being supplied to the operating section) in a specific application of the prior art to image recognition or the like. Also, the learning function is redundant in applications to a practical system.